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Lucy Moctezuma Tan, Faye Orcales, Pleuni Simone Pennings
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Comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper”
10.1371/journal.pcbi.1013673 · 2025
Using genomic data and machine learning to predict antibiotic resistance: a tutorial paper
10.1371/journal.pcbi.1012579 · 2024
Challenges in the real world use of classification accuracy metrics: from recall and precision to the Matthews correlation coefficient
10.1371/journal.pone.0291908 · 2023
Limitations in evaluating machine learning models for imbalanced binary outcome classification in spine surgery: a systematic review
10.3390/brainsci13121723 · 2023
Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
10.1371/journal.pcbi.1006258 · 2018
Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning
10.1038/s41598-025-14078-w · 2025
Navigating the pitfalls of applying machine learning in genomics
10.1038/s41576-021-00434-9 · 2022
A new clone sweeps clean: the enigmatic emergence of Escherichia coli sequence type 131
10.1128/aac.02824-14 · 2014
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
Provenance
crossref
Confidence 100%
ror
Confidence 99%
pubmed
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europepmc
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openalex
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doaj
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datacite
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10.15252/emmm.201910264 · 2020
The advantage of intergenic regions as genomic features for machine-learning-based host attribution of Salmonella Typhimurium from the USA
2023
Next-generation diagnostics of bloodstream infections enabled by rapid whole-genome sequencing of bacterial cells purified from blood cultures
10.1016/j.ebiom.2025.105633 · 2025
Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective
2022
Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa
10.1186/s12864-024-10214-4 · 2024
Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa
10.1186/s12864-024-10214-4 · doi-reference
Next-generation diagnostics of bloodstream infections enabled by rapid whole-genome sequencing of bacterial cells purified from blood cultures
10.1016/j.ebiom.2025.105633 · doi-reference
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
10.15252/emmm.201910264 · doi-reference
A new clone sweeps clean: the enigmatic emergence of Escherichia coli sequence type 131
10.1128/aac.02824-14 · doi-reference
Navigating the pitfalls of applying machine learning in genomics
10.1038/s41576-021-00434-9 · doi-reference
Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning
10.1038/s41598-025-14078-w · doi-reference
Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
10.1371/journal.pcbi.1006258 · doi-reference
Limitations in evaluating machine learning models for imbalanced binary outcome classification in spine surgery: a systematic review
10.3390/brainsci13121723 · doi-reference
Challenges in the real world use of classification accuracy metrics: from recall and precision to the Matthews correlation coefficient
10.1371/journal.pone.0291908 · doi-reference
Using genomic data and machine learning to predict antibiotic resistance: a tutorial paper
10.1371/journal.pcbi.1012579 · doi-reference
Comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper”
10.1371/journal.pcbi.1013673 · doi-reference